Overview

Dataset statistics

Number of variables15
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.1 KiB
Average record size in memory329.0 B

Variable types

DateTime1
Categorical4
Numeric10

Alerts

year has constant value ""Constant
Temperature is highly overall correlated with RH and 7 other fieldsHigh correlation
RH is highly overall correlated with Temperature and 4 other fieldsHigh correlation
Rain is highly overall correlated with FFMC and 5 other fieldsHigh correlation
FFMC is highly overall correlated with Temperature and 8 other fieldsHigh correlation
DMC is highly overall correlated with Temperature and 8 other fieldsHigh correlation
DC is highly overall correlated with Temperature and 7 other fieldsHigh correlation
ISI is highly overall correlated with Temperature and 8 other fieldsHigh correlation
BUI is highly overall correlated with Temperature and 7 other fieldsHigh correlation
FWI is highly overall correlated with Temperature and 8 other fieldsHigh correlation
day is highly overall correlated with DMC and 1 other fieldsHigh correlation
month is highly overall correlated with Temperature and 3 other fieldsHigh correlation
Ws is highly overall correlated with Temperature and 1 other fieldsHigh correlation
Classes is highly overall correlated with Temperature and 6 other fieldsHigh correlation
day is uniformly distributedUniform
Rain has 133 (54.7%) zerosZeros
ISI has 4 (1.6%) zerosZeros
FWI has 9 (3.7%) zerosZeros

Reproduction

Analysis started2023-02-18 21:51:37.099653
Analysis finished2023-02-18 21:52:22.615347
Duration45.52 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Date
Date

Distinct122
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2012-06-01 00:00:00
Maximum2012-09-30 00:00:00
2023-02-19T03:22:23.079063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:23.327359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

day
Categorical

HIGH CORRELATION  UNIFORM 

Distinct31
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size14.1 KiB
1
 
8
17
 
8
30
 
8
29
 
8
28
 
8
Other values (26)
203 

Length

Max length2
Median length2
Mean length1.7037037
Min length1

Characters and Unicode

Total characters414
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 8
 
3.3%
17 8
 
3.3%
30 8
 
3.3%
29 8
 
3.3%
28 8
 
3.3%
27 8
 
3.3%
26 8
 
3.3%
25 8
 
3.3%
24 8
 
3.3%
23 8
 
3.3%
Other values (21) 163
67.1%

Length

2023-02-19T03:22:23.514827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 8
 
3.3%
17 8
 
3.3%
3 8
 
3.3%
4 8
 
3.3%
5 8
 
3.3%
6 8
 
3.3%
7 8
 
3.3%
8 8
 
3.3%
9 8
 
3.3%
10 8
 
3.3%
Other values (21) 163
67.1%

Most occurring characters

ValueCountFrequency (%)
1 107
25.8%
2 104
25.1%
3 36
 
8.7%
7 24
 
5.8%
0 24
 
5.8%
9 24
 
5.8%
8 24
 
5.8%
6 24
 
5.8%
5 24
 
5.8%
4 23
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 414
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 107
25.8%
2 104
25.1%
3 36
 
8.7%
7 24
 
5.8%
0 24
 
5.8%
9 24
 
5.8%
8 24
 
5.8%
6 24
 
5.8%
5 24
 
5.8%
4 23
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 414
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 107
25.8%
2 104
25.1%
3 36
 
8.7%
7 24
 
5.8%
0 24
 
5.8%
9 24
 
5.8%
8 24
 
5.8%
6 24
 
5.8%
5 24
 
5.8%
4 23
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 107
25.8%
2 104
25.1%
3 36
 
8.7%
7 24
 
5.8%
0 24
 
5.8%
9 24
 
5.8%
8 24
 
5.8%
6 24
 
5.8%
5 24
 
5.8%
4 23
 
5.6%

month
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
8
62 
7
61 
6
60 
9
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Length

2023-02-19T03:22:23.699808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-19T03:22:24.050822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring characters

ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 243
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 243
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

year
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2012
243 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 243
100.0%

Length

2023-02-19T03:22:24.216605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-19T03:22:24.379913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2012 243
100.0%

Most occurring characters

ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Temperature
Real number (ℝ)

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.152263
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:24.520504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6280395
Coefficient of variation (CV)0.11283932
Kurtosis-0.14141446
Mean32.152263
Median Absolute Deviation (MAD)3
Skewness-0.19132733
Sum7813
Variance13.16267
MonotonicityNot monotonic
2023-02-19T03:22:24.703265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 29
11.9%
31 25
10.3%
34 24
9.9%
33 23
9.5%
30 22
9.1%
32 21
8.6%
36 21
8.6%
29 18
7.4%
28 15
6.2%
27 8
 
3.3%
Other values (9) 37
15.2%
ValueCountFrequency (%)
22 2
 
0.8%
24 3
 
1.2%
25 6
 
2.5%
26 5
 
2.1%
27 8
 
3.3%
28 15
6.2%
29 18
7.4%
30 22
9.1%
31 25
10.3%
32 21
8.6%
ValueCountFrequency (%)
42 1
 
0.4%
40 3
 
1.2%
39 6
 
2.5%
38 3
 
1.2%
37 8
 
3.3%
36 21
8.6%
35 29
11.9%
34 24
9.9%
33 23
9.5%
32 21
8.6%

RH
Real number (ℝ)

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.041152
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:24.913191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816
Coefficient of variation (CV)0.23900523
Kurtosis-0.50894281
Mean62.041152
Median Absolute Deviation (MAD)11
Skewness-0.24279046
Sum15076
Variance219.87433
MonotonicityNot monotonic
2023-02-19T03:22:25.131925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 10
 
4.1%
55 10
 
4.1%
58 8
 
3.3%
54 8
 
3.3%
78 8
 
3.3%
68 7
 
2.9%
66 7
 
2.9%
73 7
 
2.9%
80 7
 
2.9%
65 7
 
2.9%
Other values (52) 164
67.5%
ValueCountFrequency (%)
21 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
29 1
 
0.4%
31 1
 
0.4%
33 2
0.8%
34 3
1.2%
35 1
 
0.4%
36 1
 
0.4%
37 3
1.2%
ValueCountFrequency (%)
90 1
 
0.4%
89 3
1.2%
88 3
1.2%
87 4
1.6%
86 3
1.2%
84 2
 
0.8%
83 1
 
0.4%
82 3
1.2%
81 6
2.5%
80 7
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.493827
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:25.352173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8113853
Coefficient of variation (CV)0.18145196
Kurtosis2.6217035
Mean15.493827
Median Absolute Deviation (MAD)2
Skewness0.55558584
Sum3765
Variance7.9038874
MonotonicityNot monotonic
2023-02-19T03:22:25.508971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
14 43
17.7%
15 40
16.5%
13 30
12.3%
17 28
11.5%
16 27
11.1%
18 25
10.3%
19 15
 
6.2%
21 8
 
3.3%
11 7
 
2.9%
12 7
 
2.9%
Other values (8) 13
 
5.3%
ValueCountFrequency (%)
6 1
 
0.4%
8 1
 
0.4%
9 2
 
0.8%
10 3
 
1.2%
11 7
 
2.9%
12 7
 
2.9%
13 30
12.3%
14 43
17.7%
15 40
16.5%
16 27
11.1%
ValueCountFrequency (%)
29 1
 
0.4%
26 1
 
0.4%
22 2
 
0.8%
21 8
 
3.3%
20 2
 
0.8%
19 15
 
6.2%
18 25
10.3%
17 28
11.5%
16 27
11.1%
15 40
16.5%

Rain
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76296296
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:25.733966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.0032068
Coefficient of variation (CV)2.6255623
Kurtosis25.822987
Mean0.76296296
Median Absolute Deviation (MAD)0
Skewness4.5686298
Sum185.4
Variance4.0128375
MonotonicityNot monotonic
2023-02-19T03:22:25.921480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.7 6
 
2.5%
0.6 6
 
2.5%
0.5 5
 
2.1%
1.1 3
 
1.2%
1.2 3
 
1.2%
Other values (29) 40
 
16.5%
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.5 5
 
2.1%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.8 2
 
0.8%
0.9 1
 
0.4%
ValueCountFrequency (%)
16.8 1
0.4%
13.1 1
0.4%
10.1 1
0.4%
8.7 1
0.4%
8.3 1
0.4%
7.2 1
0.4%
6.5 1
0.4%
6 1
0.4%
5.8 1
0.4%
4.7 1
0.4%

FFMC
Real number (ℝ)

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.842387
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:26.217708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.349641
Coefficient of variation (CV)0.18434226
Kurtosis1.040087
Mean77.842387
Median Absolute Deviation (MAD)5.8
Skewness-1.3201301
Sum18915.7
Variance205.9122
MonotonicityNot monotonic
2023-02-19T03:22:26.688958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 7
 
2.9%
89.4 5
 
2.1%
89.3 4
 
1.6%
85.4 4
 
1.6%
89.1 4
 
1.6%
78.3 3
 
1.2%
88.1 3
 
1.2%
88.3 3
 
1.2%
47.4 3
 
1.2%
79.9 3
 
1.2%
Other values (163) 204
84.0%
ValueCountFrequency (%)
28.6 1
0.4%
30.5 1
0.4%
36.1 1
0.4%
37.3 1
0.4%
37.9 1
0.4%
40.9 1
0.4%
41.1 1
0.4%
42.6 1
0.4%
44.9 1
0.4%
45 1
0.4%
ValueCountFrequency (%)
96 1
0.4%
94.3 1
0.4%
94.2 1
0.4%
93.9 2
0.8%
93.8 1
0.4%
93.7 1
0.4%
93.3 1
0.4%
93 1
0.4%
92.5 2
0.8%
92.2 2
0.8%

DMC
Real number (ℝ)

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680658
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:27.163364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39304
Coefficient of variation (CV)0.84417465
Kurtosis2.462551
Mean14.680658
Median Absolute Deviation (MAD)6.9
Skewness1.5229829
Sum3567.4
Variance153.58743
MonotonicityNot monotonic
2023-02-19T03:22:27.586176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.1%
12.5 4
 
1.6%
1.9 4
 
1.6%
3.4 3
 
1.2%
4.6 3
 
1.2%
8.3 3
 
1.2%
16.5 3
 
1.2%
3.2 3
 
1.2%
16 3
 
1.2%
6 3
 
1.2%
Other values (155) 209
86.0%
ValueCountFrequency (%)
0.7 1
 
0.4%
0.9 2
0.8%
1.1 2
0.8%
1.2 1
 
0.4%
1.3 3
1.2%
1.7 1
 
0.4%
1.9 4
1.6%
2.1 1
 
0.4%
2.2 2
0.8%
2.4 1
 
0.4%
ValueCountFrequency (%)
65.9 1
0.4%
61.3 1
0.4%
56.3 1
0.4%
54.2 1
0.4%
51.3 1
0.4%
50.2 1
0.4%
47 1
0.4%
46.6 1
0.4%
46.1 1
0.4%
45.6 1
0.4%

DC
Real number (ℝ)

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430864
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:28.048587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.665606
Coefficient of variation (CV)0.96428834
Kurtosis1.5964668
Mean49.430864
Median Absolute Deviation (MAD)23.9
Skewness1.4734602
Sum12011.7
Variance2272.01
MonotonicityNot monotonic
2023-02-19T03:22:28.265259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
2.1%
7.6 4
 
1.6%
7.8 4
 
1.6%
8.4 4
 
1.6%
7.5 4
 
1.6%
8.3 4
 
1.6%
8.2 4
 
1.6%
17 3
 
1.2%
16.6 2
 
0.8%
33.1 2
 
0.8%
Other values (187) 207
85.2%
ValueCountFrequency (%)
6.9 1
 
0.4%
7 2
0.8%
7.1 1
 
0.4%
7.3 2
0.8%
7.4 2
0.8%
7.5 4
1.6%
7.6 4
1.6%
7.7 2
0.8%
7.8 4
1.6%
7.9 1
 
0.4%
ValueCountFrequency (%)
220.4 1
0.4%
210.4 1
0.4%
200.2 1
0.4%
190.6 1
0.4%
181.3 1
0.4%
180.4 1
0.4%
177.3 1
0.4%
171.3 1
0.4%
168.2 1
0.4%
167.2 1
0.4%

ISI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7423868
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:28.468373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.1542338
Coefficient of variation (CV)0.87597954
Kurtosis0.86232522
Mean4.7423868
Median Absolute Deviation (MAD)2.4
Skewness1.1402426
Sum1152.4
Variance17.257659
MonotonicityNot monotonic
2023-02-19T03:22:28.754587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
3.3%
1.2 7
 
2.9%
0.4 5
 
2.1%
4.7 5
 
2.1%
5.2 5
 
2.1%
1.5 5
 
2.1%
2.8 5
 
2.1%
1 5
 
2.1%
5.6 5
 
2.1%
2.2 4
 
1.6%
Other values (96) 189
77.8%
ValueCountFrequency (%)
0 4
1.6%
0.1 4
1.6%
0.2 4
1.6%
0.3 3
1.2%
0.4 5
2.1%
0.5 2
 
0.8%
0.6 4
1.6%
0.7 4
1.6%
0.8 3
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
19 1
0.4%
18.5 1
0.4%
17.2 1
0.4%
16.6 1
0.4%
16 1
0.4%
15.7 2
0.8%
15.5 1
0.4%
14.3 1
0.4%
14.2 1
0.4%
13.8 2
0.8%

BUI
Real number (ℝ)

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.690535
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:29.003672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.228421
Coefficient of variation (CV)0.85248443
Kurtosis1.9560166
Mean16.690535
Median Absolute Deviation (MAD)7.3
Skewness1.4527448
Sum4055.8
Variance202.44797
MonotonicityNot monotonic
2023-02-19T03:22:29.225561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.1%
5.1 4
 
1.6%
8.3 3
 
1.2%
2.9 3
 
1.2%
11.5 3
 
1.2%
2.4 3
 
1.2%
7.7 3
 
1.2%
14.1 3
 
1.2%
4.4 3
 
1.2%
3.9 3
 
1.2%
Other values (163) 210
86.4%
ValueCountFrequency (%)
1.1 1
 
0.4%
1.4 2
0.8%
1.6 2
0.8%
1.7 2
0.8%
1.8 2
0.8%
2.2 1
 
0.4%
2.4 3
1.2%
2.6 2
0.8%
2.7 2
0.8%
2.8 2
0.8%
ValueCountFrequency (%)
68 1
0.4%
67.4 1
0.4%
64 1
0.4%
62.9 1
0.4%
59.5 1
0.4%
59.3 1
0.4%
57.1 1
0.4%
54.9 1
0.4%
54.7 1
0.4%
50.9 1
0.4%

FWI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-02-19T03:22:29.528610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.4405677
Coefficient of variation (CV)1.0575912
Kurtosis0.65498526
Mean7.0353909
Median Absolute Deviation (MAD)3.8
Skewness1.1475925
Sum1709.6
Variance55.362048
MonotonicityNot monotonic
2023-02-19T03:22:29.771529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 12
 
4.9%
0.8 10
 
4.1%
0.5 9
 
3.7%
0.1 9
 
3.7%
0 9
 
3.7%
0.3 8
 
3.3%
0.9 7
 
2.9%
0.2 6
 
2.5%
0.7 5
 
2.1%
0.6 4
 
1.6%
Other values (115) 164
67.5%
ValueCountFrequency (%)
0 9
3.7%
0.1 9
3.7%
0.2 6
2.5%
0.3 8
3.3%
0.4 12
4.9%
0.5 9
3.7%
0.6 4
 
1.6%
0.7 5
2.1%
0.8 10
4.1%
0.9 7
2.9%
ValueCountFrequency (%)
31.1 1
0.4%
30.3 1
0.4%
30.2 1
0.4%
30 1
0.4%
26.9 1
0.4%
26.3 1
0.4%
26.1 1
0.4%
25.4 1
0.4%
24.5 1
0.4%
24 1
0.4%

Classes
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
fire
137 
not fire
106 

Length

Max length8
Median length4
Mean length5.744856
Min length4

Characters and Unicode

Total characters1396
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire 137
56.4%
not fire 106
43.6%

Length

2023-02-19T03:22:29.987956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-19T03:22:30.164028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
fire 243
69.6%
not 106
30.4%

Most occurring characters

ValueCountFrequency (%)
f 243
17.4%
i 243
17.4%
r 243
17.4%
e 243
17.4%
n 106
7.6%
o 106
7.6%
t 106
7.6%
106
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1290
92.4%
Space Separator 106
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 243
18.8%
i 243
18.8%
r 243
18.8%
e 243
18.8%
n 106
8.2%
o 106
8.2%
t 106
8.2%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1290
92.4%
Common 106
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 243
18.8%
i 243
18.8%
r 243
18.8%
e 243
18.8%
n 106
8.2%
o 106
8.2%
t 106
8.2%
Common
ValueCountFrequency (%)
106
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 243
17.4%
i 243
17.4%
r 243
17.4%
e 243
17.4%
n 106
7.6%
o 106
7.6%
t 106
7.6%
106
7.6%

Interactions

2023-02-19T03:22:19.483119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:21:59.431137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:02.939817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:05.746111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:07.778337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:09.968501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:12.162344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:14.133699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.842461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.626228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:19.685900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:00.195048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:03.204663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:05.953179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:08.018636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:10.164136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:12.349588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:14.329444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.027787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.814660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:19.843408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:00.937223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:03.499993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:06.141618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:08.315619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:10.640113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:12.544400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:14.496292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.215048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.999867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.015439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:01.268987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:03.750641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:06.300296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:08.492481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:10.813588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:12.717214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:14.660335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.389526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:18.166888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.199826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:01.626628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:03.958471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:06.460742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:08.680721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.022337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:12.995291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:14.833617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.563458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:18.362736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.385657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:01.810289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:04.211894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:06.651394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:08.841699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.198697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:13.164951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.000255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.743447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:18.550072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.547558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:02.018601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:04.433766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:06.873647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:09.101116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.371711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:13.339102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.166881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:16.916991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:18.741030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.699127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:02.199171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:04.899117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:07.038003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:09.351329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.534924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:13.555741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.333840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.079976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:18.891541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:20.892295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:02.468174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:05.363053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:07.222796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:09.616931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.725992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:13.779617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.502701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.259725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:19.103159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:21.063254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:02.727745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:05.563404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:07.499729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:09.793547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:11.984005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:13.956335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:15.677337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:17.444616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:22:19.318490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-19T03:22:30.347851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
TemperatureRHWsRainFFMCDMCDCISIBUIFWI
Temperature1.000-0.651-0.285-0.3260.6770.4860.3760.6040.4600.567
RH-0.6511.0000.2440.222-0.645-0.409-0.227-0.687-0.354-0.581
Ws-0.2850.2441.0000.172-0.167-0.0010.0790.0090.0310.032
Rain-0.3260.2220.1721.000-0.544-0.289-0.298-0.347-0.300-0.324
FFMC0.677-0.645-0.167-0.5441.0000.6040.5070.7400.5920.691
DMC0.486-0.409-0.001-0.2890.6041.0000.8760.6800.9820.876
DC0.376-0.2270.079-0.2980.5070.8761.0000.5090.9420.740
ISI0.604-0.6870.009-0.3470.7400.6800.5091.0000.6440.923
BUI0.460-0.3540.031-0.3000.5920.9820.9420.6441.0000.858
FWI0.567-0.5810.032-0.3240.6910.8760.7400.9230.8581.000
2023-02-19T03:22:30.708694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
TemperatureRHWsRainFFMCDMCDCISIBUIFWI
Temperature1.000-0.643-0.224-0.2930.6660.6110.5050.6480.5860.657
RH-0.6431.0000.2010.179-0.665-0.505-0.347-0.643-0.467-0.598
Ws-0.2240.2011.0000.011-0.0670.0010.0600.0320.0270.034
Rain-0.2930.1790.0111.000-0.741-0.559-0.612-0.738-0.576-0.718
FFMC0.666-0.665-0.067-0.7411.0000.8220.7350.9890.8070.968
DMC0.611-0.5050.001-0.5590.8221.0000.8930.8220.9880.916
DC0.505-0.3470.060-0.6120.7350.8931.0000.7460.9430.849
ISI0.648-0.6430.032-0.7380.9890.8220.7461.0000.8110.975
BUI0.586-0.4670.027-0.5760.8070.9880.9430.8111.0000.911
FWI0.657-0.5980.034-0.7180.9680.9160.8490.9750.9111.000
2023-02-19T03:22:30.972757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
TemperatureRHWsRainFFMCDMCDCISIBUIFWI
Temperature1.000-0.490-0.167-0.2250.5050.4420.3660.4930.4210.492
RH-0.4901.0000.1460.133-0.509-0.358-0.239-0.486-0.328-0.438
Ws-0.1670.1461.0000.009-0.046-0.0010.0450.0240.0180.027
Rain-0.2250.1330.0091.000-0.580-0.424-0.466-0.580-0.437-0.563
FFMC0.505-0.509-0.046-0.5801.0000.6300.5520.9230.6170.856
DMC0.442-0.358-0.001-0.4240.6301.0000.7120.6300.9150.754
DC0.366-0.2390.045-0.4660.5520.7121.0000.5620.7970.669
ISI0.493-0.4860.024-0.5800.9230.6300.5621.0000.6200.875
BUI0.421-0.3280.018-0.4370.6170.9150.7970.6201.0000.750
FWI0.492-0.4380.027-0.5630.8560.7540.6690.8750.7501.000
2023-02-19T03:22:31.436725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
daymonthTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
day1.0000.0000.0000.0000.0000.2170.0000.5160.0000.0000.5090.2340.120
month0.0001.0000.5720.3660.1920.1420.4200.5160.4510.3820.5110.4360.503
Temperature0.0000.5721.0000.7570.6010.5800.7580.4830.2640.6630.4210.6660.620
RH0.0000.3660.7571.0000.2800.0000.7680.4460.1820.7800.4170.6390.537
Ws0.0000.1920.6010.2801.0000.7090.2670.0000.0000.0990.0000.0000.139
Rain0.2170.1420.5800.0000.7091.0000.6580.0000.0000.0000.0000.0000.352
FFMC0.0000.4200.7580.7680.2670.6581.0000.6260.4660.8800.6460.7690.993
DMC0.5160.5160.4830.4460.0000.0000.6261.0000.8780.7890.9580.8770.872
DC0.0000.4510.2640.1820.0000.0000.4660.8781.0000.6600.9030.7800.797
ISI0.0000.3820.6630.7800.0990.0000.8800.7890.6601.0000.7510.8980.988
BUI0.5090.5110.4210.4170.0000.0000.6460.9580.9030.7511.0000.8550.900
FWI0.2340.4360.6660.6390.0000.0000.7690.8770.7800.8980.8551.0000.979
Classes0.1200.5030.6200.5370.1390.3520.9930.8720.7970.9880.9000.9791.000
2023-02-19T03:22:31.954008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
TemperatureRHWsRainFFMCDMCDCISIBUIFWIdaymonthClasses
Temperature1.000-0.643-0.224-0.2930.6660.6110.5050.6480.5860.6570.0000.3910.500
RH-0.6431.0000.2010.179-0.665-0.505-0.347-0.643-0.467-0.5980.0000.2230.407
Ws-0.2240.2011.0000.011-0.0670.0010.0600.0320.0270.0340.0000.1220.137
Rain-0.2930.1790.0111.000-0.741-0.559-0.612-0.738-0.576-0.7180.0750.0890.347
FFMC0.666-0.665-0.067-0.7411.0000.8220.7350.9890.8070.9680.0000.2600.911
DMC0.611-0.5050.001-0.5590.8221.0000.8930.8220.9880.9160.1970.3300.693
DC0.505-0.3470.060-0.6120.7350.8931.0000.7460.9430.8490.0000.2810.621
ISI0.648-0.6430.032-0.7380.9890.8220.7461.0000.8110.9750.0000.2450.882
BUI0.586-0.4670.027-0.5760.8070.9880.9430.8111.0000.9110.1940.3260.726
FWI0.657-0.5980.034-0.7180.9680.9160.8490.9750.9111.0000.0770.2710.861
day0.0000.0000.0000.0750.0000.1970.0000.0000.1940.0771.0000.0000.093
month0.3910.2230.1220.0890.2600.3300.2810.2450.3260.2710.0001.0000.340
Classes0.5000.4070.1370.3470.9110.6930.6210.8820.7260.8610.0930.3401.000

Missing values

2023-02-19T03:22:21.348402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-19T03:22:22.232760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DatedaymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
02012-06-011620122957180.065.73.47.61.33.40.5not fire
12012-06-022620122961131.364.44.17.61.03.90.4not fire
22012-06-0336201226822213.147.12.57.10.32.70.1not fire
32012-06-044620122589132.528.61.36.90.01.70.0not fire
42012-06-055620122777160.064.83.014.21.23.90.5not fire
52012-06-066620123167140.082.65.822.23.17.02.5fire
62012-06-077620123354130.088.29.930.56.410.97.2fire
72012-06-088620123073150.086.612.138.35.613.57.1fire
82012-06-099620122588130.252.97.938.80.410.50.3not fire
92012-06-1010620122879120.073.29.546.31.312.60.9not fire
DatedaymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
2332012-09-2121920123534170.092.223.697.313.829.421.6fire
2342012-09-2222920123364130.088.926.1106.37.132.413.7fire
2352012-09-2323920123556140.089.029.4115.67.536.015.2fire
2362012-09-242492012264962.061.311.928.10.611.90.4not fire
2372012-09-2525920122870150.079.913.836.12.414.13.0not fire
2382012-09-2626920123065140.085.416.044.54.516.96.5fire
2392012-09-2727920122887154.441.16.58.00.16.20.0not fire
2402012-09-2828920122787290.545.93.57.90.43.40.2not fire
2412012-09-2929920122454180.179.74.315.21.75.10.7not fire
2422012-09-3030920122464150.267.33.816.51.24.80.5not fire
2023-02-19T03:16:15.548223 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 2023-02-19T03:16:24.687320 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:16:29.049458 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/
2023-02-19T03:16:39.309019 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ Panel
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